Controller Design for Electrical Drives by Deep Reinforcement Learning: A Proof of Concept

This article presents an approach to the controller design for electrical drives, which makes use of methods of deep reinforcement learning. Conventional control methods dominated the field for a long time, since they usually lead to control solutions with very robust and steady results. Yet, it often can be found that the overall control performance heavily correlates with the experience and education of the developing engineer. Moreover, conventional methods strongly depend on the available knowledge of the control system (e.g., plant model accuracy), which often causes the necessity for thorough identification methods. Real-time capability issues are also a present problem of sophisticated control approaches, such as model-predictive methods. Especially, in the domain of electrical drive train control, solving elaborate online optimization problems may be critical when very small plant time constants have to be considered. The methods of deep reinforcement learning will not only enable to acquire a suitable controller structure, but, moreover, the procedure will tune itself, which will allow for a more abstract level of investigation. This article presents a first proof of concept by means of controlling the phase currents of a permanent magnet synchronous motor in a field-oriented framework. The results found are promising and motivate further research in this field.